Supplementary data for the paper the Topicality Boosts Popularity: A Comparative Analysis of NYT Articles and Reddit Memes - Barnes et al. (2024)
@article{barnes2024topicality,
title={Topicality boosts popularity: a comparative analysis of NYT articles and Reddit memes},
author={Barnes, Kate and Juh{'a}sz, P{'e}ter and Nagy, Marcell and Molontay, Roland},
journal={Social Network Analysis and Mining},
volume={14},
number={1},
pages={119},
year={2024},
publisher={Springer}
}
The Reddit data was collected using the Pushshift API, and it consists of all posts starting from January 1st, 2018 to November 14th, 2022 from the r/memes subreddits.
The New York Times data was collected using the NYT Archive API, and it consists of all articles published in the same time frame.
Open access to both data sets in the data folder.
A total of 255,783 NYT articles were distilled into 2 sets of 120 topics using the BERTopic and LDA algorithms.
After cleaning steps, the Reddit data set included 899,766 meme posts. We used several meta data features and engineered novel features from the meme images:
| Feature | Type | Description |
|---|---|---|
| score | int | upvotes - downvotes |
| comments | int | number of comments |
| viral | binary | indicates if meme score is in top 5% |
| date | string | date on which meme was posted |
| day-of-week | category | day on which meme was posted |
| all-text | string | title, BLIP image caption, OCR text |
| title length | int | number of characters in title |
| thumbnail size | float | size of image thumbnail |
| over18 | binary | Reddit content warning |
| emoji | binary | identifies emojis in text |
| sentiment | float | text valence score |
| HSV/RGB | floats | mean image values |
| 10 colors | floats | normalized pixels of color in image |
| face | binary | indicates face in image |
| topic | int | highest probability topic assigned to meme |
| probability | float | topic's probability |
| entropy | float | entropy of topic distribution assigned to meme |
| monthly | float | monthly average topicality |
| daily | float | daily average topicality |
| slope | float | slope of topicality distribution |
Python code files with exploratory analysis of the data sets, feature extraction, topic analysis and popularity prediction are available in the code folder.
The 5-year daily distributions of all topics explored in this project can be found in the figures folder or at this link. Daily and monthly distributions for 23 of these topics are displayed and discussed in more detail in the article.